The discriminative dataless methods mainly extend traditional discriminative methods to train classifiers over unlabeled corpora with only seed words. An early method proposed in [1]} manually selects seed words by referring to their information gain values, which are computed on the k-means clustering results over unlabeled documents. The method employs the selected seed words to construct a pseudo training dataset, and then iteratively train a naive Bayes classifier on it using semi-supervised expectation maximization. A recent method, named pseudo-label based dataless naive Bayes (PL-DNB) [2]}, simultaneously trains the naive Bayes classifier and updates the pseudo training dataset using both the seed word information and temporary prediction results. Additionally, the maximum entropy-based dataless classifier [3]} supposes that a document is more likely to belong to the categories, whose seed words have occurred in this document. It builds an objective of the distance between the expected label distributions of documents containing seed words and the corresponding reference label distributions of seed words. Another interesting work [4]} trains a dataless classifier using Wikipedia concepts.
